text: # -*- coding: utf-8 -*-
  from __future__ import division, print_function, absolute_import
  
  import numpy as np
  
  from libs.configs._base_.models.retinanet_r50_fpn import *
  from libs.configs._base_.datasets.dota_detection import *
  from libs.configs._base_.schedules.schedule_1x import *
  from dataloader.pretrained_weights.pretrain_zoo import PretrainModelZoo
  
  # schedule
  BATCH_SIZE = 1
  GPU_GROUP = '0,1,2,3'
  NUM_GPU = len(GPU_GROUP.strip().split(','))
  LR = 1e-3
  SAVE_WEIGHTS_INTE = 10000
  DECAY_STEP = np.array(DECAY_EPOCH, np.int32) * SAVE_WEIGHTS_INTE
  MAX_ITERATION = SAVE_WEIGHTS_INTE * MAX_EPOCH
  WARM_SETP = int(WARM_EPOCH * SAVE_WEIGHTS_INTE)
  
  # dataset
  DATASET_NAME = 'HRSC2016'
  IMG_SHORT_SIDE_LEN = 512
  IMG_MAX_LENGTH = 512
  CLASS_NUM = 1
  
  # data augmentation
  IMG_ROTATE = True
  RGB2GRAY = True
  VERTICAL_FLIP = True
  HORIZONTAL_FLIP = True
  IMAGE_PYRAMID = False
  
  # model
  pretrain_zoo = PretrainModelZoo()
  PRETRAINED_CKPT = pretrain_zoo.pretrain_weight_path(NET_NAME, ROOT_PATH)
  TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights')
  
  # loss
  CLS_WEIGHT = 1.0
  REG_WEIGHT = 1.0
  REG_LOSS_MODE = 2  # GWD loss
  
  GWD_TAU = 2.0
  GWD_FUNC = tf.sqrt
  
  VERSION = 'RetinaNet_HRSC2016_GWD_1x_20201220'
  
  """
  RetinaNet-H + gwd fix bug (wasserstein_distance = tf.maximum(tf.log(wasserstein_distance + 1e-3), 0.0))
  + sqrt tau=2
  FLOPs: 472715470;    Trainable params: 32325246
  cls : ship|| Recall: 0.9397394136807817 || Precison: 0.6494091164884637|| AP: 0.8556218502153536
  F1:0.8895116092874299 P:0.8748031496062992 R:0.9047231270358306
  mAP is : 0.8556218502153536
  
  85.56  84.97  84.04  75.99  73.32  60.31  44.39  17.14  3.13  0.06
  
  SWA
  cls : ship|| Recall: 0.9372964169381107 || Precison: 0.6355604638321369|| AP: 0.8533035469890232
  F1:0.8935433875930093 P:0.8849840255591054 R:0.9022801302931596
  mAP is : 0.8533035469890232
  
  85.33  85.02  84.07  75.30  73.97  62.71  45.65  21.73  3.1  0.36
  """
  
  
  